Automated precision alignment of data in a utility monitoring system

ABSTRACT

A data alignment algorithm that automatically aligns data from multiple monitoring devices to the same zero-crossings at the same point in time. Cycle-by-cycle frequency data is received from each monitoring device and a cross-correlation algorithm is performed to determine a correlation coefficient between a reference monitoring device and another monitoring device. The data of the other monitoring device is shifted by one cycle and another correlation coefficient is calculated by the cross-correlation algorithm. The data of the two monitoring devices is aligned at the point at which the maximum correlation coefficient is calculated or the point at which the correlation coefficient exceeds a threshold value. The clocks of the monitoring devices can also be synchronized at the same point of alignment.

FIELD OF THE INVENTION

The present invention relates generally to utility monitoring systems,and, in particular, to automated precision alignment of data, automateddetermination of power monitoring system hierarchy, and automatedintegration of data in a utility monitoring system.

BACKGROUND OF THE INVENTION

Since the introduction of electrical power distribution systems in thelate 19^(th) century, there has been a need to monitor their operationaland electrical characteristics. The ability to collect, analyze, andrespond to information about the electrical power system can improvesafety, minimize equipment loss, decrease scrap, and ultimately savetime and money. To that end, monitoring devices were developed tomeasure and report such information. With the dawn of the electronicsage, the quality and quantity of data from monitoring devices was vastlyimproved, and communications networks and software were developed tocollect, display and store information. Unfortunately, those responsiblefor evaluating data from monitoring devices are now overwhelmed byinformation from their monitoring systems. In the endeavor to maximizethe usefulness of a monitoring system, monitoring equipmentmanufacturers are seeking methods of presenting information in the mostuseful format.

Effectively monitoring today's electrical power distribution systems iscumbersome, expensive, and inefficient. Electric power monitoringsystems are typically arranged in a hierarchy with monitoring devicessuch as electrical meters installed at various levels of the hierarchy(refer to FIG. 2). Monitoring devices measure various characteristics ofthe electrical signal (e.g., voltage, current, waveform distortion,power, etc.) passing through the conductors, and the data from eachmonitoring device is analyzed by the user to evaluate potentialperformance or quality-related issues. However, the components oftoday's electrical monitoring systems (monitoring devices, software,etc.) act independently of each other, requiring the user to be anexpert at configuring hardware, collecting and analyzing data, anddetermining what data is vital or useful. There are two problems here:the amount of data to be analyzed and the context of the data. These areseparate but related issues. It is possible to automate the analysis ofthe data to address the amount of data. But, in order to do thisreliably, the data must be put into context. The independence of databetween each monitoring device evaluating the electrical systemessentially renders each monitoring device oblivious of data from othermonitoring devices connected to the system being analyzed. Accordingly,the data transmitted to the system computer from each monitoring deviceis often misaligned in that data from each monitoring device on thesystem does not arrive at the monitoring system's computersimultaneously. There are two basic reasons for the temporalmisalignment of data between monitoring devices: communications timedelays and monitoring device timekeeping & event time stamping. It isthen up to the user to analyze and interpret this independent data inorder to optimize performance or evaluate potential quality-relatedconcerns on the electrical system.

Sophisticated processing capabilities in digital monitoring devicesallow large amounts of complex electrical data to be derived andaccumulated from a seemingly simple electrical signal. Because of thedata's complexity, quantity, and relative disjointed relationship fromone monitoring device to the next, manual analysis of all the data is anenormous effort that often requires experts to be hired to complete thetask. This process is tedious, complex, prone to error and oversight,and time-consuming. A partial solution has been to use globalpositioning satellite (GPS) systems to timestamp an event, but thisapproach requires that the user purchase and install additional hardwareand data lines to link the monitoring devices together. And thissolution still requires the evaluation of large amounts of data becausethe system is only temporally in context; not spatially in context.Synchronizing data using GPS systems is also disadvantageous because oftime delays associated with other hardware in the system. Furthermore,any alignment of data by a GPS-based system can only be as accurate asthe propagation delay of the GPS signal, which means that the data stillmay not be optimally aligned when a GPS system is used.

The addition of supplemental monitoring devices in the electrical systemdoes nothing more than generate more information about the electricalsystem at the point where the meter is added in the electrical system,increasing complexity without any benefit. Any usefulness of the data isgenerally limited to the locality of the monitoring device that wasadded, while even more data is amassed.

The complexity of many electrical systems usually necessitates aninvolved configuration process of monitoring systems because eachmetered point in the electrical system has different characteristics,which is why multiple monitoring devices are installed in the firstplace. As a result of the enormous volume of complex data accumulatedfrom electrical monitoring systems heretofore, a thorough analysis ofthe data is typically not feasible due to limited resources, time,and/or experience.

Temporal alignment of the data is one important aspect to understand andcharacterize the power system. Another important aspect is having athorough knowledge of the power monitoring system's layout (orhierarchy). Power monitoring devices measure the electrical system'soperating parameters, but do not provide information about how theparameters at different points on the power monitoring system relate toeach other. Knowing the hierarchy of the power monitoring system putsthe operating parameters of multiple monitoring devices into contextwith each other.

To determine the layout of a power monitoring system, a user must reviewelectrical one-line drawings or physically perform an inventory of theelectrical system if one-line drawings are unavailable. The usermanually enters the spatial information into the monitoring systemsoftware for analysis. When a new device or monitored load is added ormoved within the power monitoring system, the user must manually updatethe monitoring system software to reflect the new addition or change.

Data alignment and layout information are essential to understanding andcharacterizing the power system. With these two pieces of information,the data from each meter can be integrated and put into context withevery other meter in the power system. Heretofore, the only techniquesfor passably integrating data were complex, expensive, manuallyintensive, and time-consuming for the user. These techniques also permitonly limited integration of data and require additional hardware (suchas GPS hardware), data lines, and supplemental monitoring deviceaccessories.

What is needed, therefore, is an automated data integration technique,including automatic precision alignment of data and automatichierarchical classification of system layout. The present invention isdirected to satisfying this and other needs.

SUMMARY OF THE INVENTION

Briefly, according to an embodiment of the present invention, a methodof aligning data measured by monitoring devices coupled to a powermonitoring system includes receiving reference signal data from areference monitoring device. The reference signal data representsfrequency variations measured by the reference monitoring device for apredetermined number of cycles. The method further includes receivingsecond signal data from a second monitoring device that measuresfrequency variations for a predetermined number of cycles. The methodfurther includes automatically aligning the reference signal data withthe second signal data.

According to another embodiment of the present invention, theautomatically aligning includes computing a correlation coefficientproduced by a cross-correlation algorithm using the reference signaldata and the second signal data. The automatically aligning furtherincludes determining whether a maximum correlation coefficient isproduced by shifting the second signal data relative to the referencesignal data and computing a correlation coefficient produced by thecross-correlation algorithm using the shifted second signal data and thereference signal data. The automatically aligning further includesrepeating the determining until a maximum correlation coefficient isproduced by the cross-correlation algorithm. The cross-correlationalgorithm can be a circular or linear cross-correlation algorithm inembodiments of the present invention.

According to various embodiments of the present invention, the methodmay further include communicating an instruction to the referencemonitoring device to buffer the reference signal data for thepredetermined number of cycles. The method may further include providingreference time data, receiving first time data from the referencemonitoring device, and synchronizing the first time data with thereference time data. The method may further include sampling data at thezero-crossing of a reference channel associated with the referencemonitoring device, determining whether the values of the sampled dataare zero, negative, or positive, assigning phase notations based on thedetermining, and displaying information representing the phase notationsto the user. Optionally, the user can be alerted when the phasenotations are misidentified on a phase conductor.

According to still another embodiment of the present invention,monitoring system software sends an instruction or message to monitoringdevices in a power monitoring system to begin buffering data (preferablydata indicative of fundamental frequency variations). The monitoringsystem software reads the data from each monitoring device and selects areference monitoring device and another monitoring device to analyze.The data between the two monitoring devices are cross-correlated using acircular or linear cross-correlation algorithm, for example. The cyclecount and time relationships between the two devices are stored in amatrix. When all devices have been analyzed and their respective dataaligned relative to one another, the monitoring system software analyzesthe voltage (or current) data for mis-wirings. If a mis-wiring isdetected, the user is notified.

The foregoing and additional aspects of the present invention will beapparent to those of ordinary skill in the art in view of the detaileddescription of various embodiments, which is made with reference to thedrawings, a brief description of which is provided next.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other advantages of the invention will become apparentupon reading the following detailed description and upon reference tothe drawings.

FIG. 1 is functional block diagram of an automated data integrationmonitoring system in accordance with the present invention;

FIG. 2 is a functional block diagram of a simplified power monitoringsystem;

FIG. 3 is a functional block diagram of a monitoring device inaccordance with an embodiment of the present invention;

FIG. 4 are exemplary frequency data samples from two monitoring devicesthat are aligned in accordance with the present invention;

FIG. 5A is a flow chart diagram of a data alignment algorithm inaccordance with an embodiment of the present invention;

FIG. 5B is a flow chart diagram of a data alignment algorithm inaccordance with another embodiment of the present invention;

FIG. 6 is a functional block diagram of a simplified hierarchy with asingle main and two feeders;

FIG. 7 is an exemplary diagram of a single radial-fed system;

FIG. 8 is an exemplary diagram of a multiple radial-fed system;

FIGS. 9-11A is a flow chart diagram of an auto-learned hierarchyalgorithm in accordance with an embodiment of the present invention;

FIG. 11B is a flow chart diagram of an auto-learned hierarchy algorithmin accordance with another embodiment of the present invention;

FIG. 11C is a flow chart diagram of an auto-learned hierarchy algorithmin accordance with still another embodiment of the present invention;and

FIG. 12 is a flow chart diagram of an automated integrated monitoringalgorithm in accordance with an embodiment of the present invention.

While the invention is susceptible to various modifications andalternative forms, specific embodiments have been shown by way ofexample in the drawings and will be described in detail herein. Itshould be understood, however, that the invention is not intended to belimited to the particular forms disclosed. Rather, the invention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention as defined by the appended claims.

DETAILED DESCRIPTION OF THE ILLUSTRATED EMBODIMENTS

Turning now to FIG. 1, an automated data integrated monitoring system100 is generally shown. A utility system 102 having multiple monitoringdevices M provides data from each monitoring device M that iscommunicated to an automated data alignment system 104 and an automatedhierarchy classification system 106. The data is aligned automaticallyin the automated data alignment system 104 in accordance with thepresent invention and produces data that is aligned such that itrepresents the data when it was actually seen simultaneously by themonitoring devices M in the power monitoring system 102. The hierarchyclassification system 106 automatically learns the hierarchy ofmonitoring devices present in the utility system 102 and theirrelationships relative to one another.

Once the data from each monitoring device M is aligned and eachmonitoring device's location is known, the data is said to be in context108. The contextual data 108 can be used by software applications 110 toprovide and diagnose useful information about the utility system 102beyond what is generally available if the data is not in context. Theutility being monitored in the utility system 102 can be any of the fiveutilities designated by the acronym, WAGES, or water, air, gas,electricity, or steam. Each monitoring device measures characteristicsof the utility, and quantifies these characteristics into data that canbe analyzed by a computer.

A user interacts with the software applications 110 via a conventionaluser interface 112. The software applications 110 can be linked to othersystems 114, such as a billing system, and use the contextual data 108to communicate messages between the other systems 114 and the userinterface 112.

The data alignment system 104 aligns data, such as voltage, current,time, events, and the like, from multiple monitoring devices M in autility system, and is a valuable tool for users. When data from all themonitoring devices M is aligned to the same point in time that the dataoccurred, the data can be put into a temporal context from whichadditional decisions regarding hardware and software configuration canbe automatically made or recommended. As used herein, a monitoringdevice refers to any system element or apparatus with the ability tosample, collect, or measure one or more operational characteristics orparameters of a utility system 102. When the utility system 102 is apower monitoring system, the monitoring device M can be a meter thatmeasures electrical characteristics or parameters of the powermonitoring system.

The data alignment techniques (which are detailed below) according tovarious aspects of the present invention accomplish at least thefollowing:

1) Automated alignment of data in monitoring devices;

2) Automated synchronization of time in monitoring devices;

3) Alignment of data and time in monitoring devices located at differentpoints on the power utility grid (where the monitoring system softwaremay obtain time data from the Internet or another server); and

4) Diagnosing misidentification or mislabeling of phases throughout theelectrical power system.

All real-world electrical signals in power systems experience subtlevariations in their frequency and amplitude over time. This variation ofthe signal's frequency and amplitude are both indeterminate and uniquewith respect to time. Each monitoring device located on the same utilitygrid will simultaneously experience the same frequency variations.Analysis of data from monitoring devices that are directly linked toeach other in the hierarchy will reveal a correlation in their amplitudevariations. Analysis of both the frequency and amplitude variations ofthe signal are then used to precisely align the data of one monitoringdevice with respect to another device (or all the monitoring devices toeach other) in the data alignment system 104. The details of the dataalignment system 104 are discussed below.

The data alignment techniques of the present invention allow allmonitoring devices M in a power utility system hierarchy to be alignedto the zero-crossing of all three phase voltages without the use ofadditional hardware. The present invention also anticipates potentialphase shifts between various monitoring devices, for example, thosecaused by certain transformer configurations. Once the data of themonitoring devices are aligned with each other, the system data isessentially aligned with respect to the time it occurred, making morecomplex data analyses feasible.

A simplified configuration of a power monitoring system 120 is shown inFIG. 2. The power monitoring system 120 includes a main 122 connected toa first load 124 by a first feeder and to a second load 126 by a secondfeeder. Monitoring devices 128, 130 measure electrical characteristicsor parameters associated with the first and second feeders,respectively. Each monitoring device 128, 130 is communicatively coupledto a computer 132.

The first monitoring device 128 can be a power meter (or electricmeter), such as shown in FIG. 3. The monitoring device 128 includes acontroller 134, firmware 136, memory 138, a communications interface140, and three phase voltage conductor connectors 142 a,b,c, whichconnect to the V_(A), V_(B), and V_(C) phase voltage conductors,respectively, and are coupled to the controller 134. Three phase currentconductor connectors 143 a,b,c, which connect to the I_(A), I_(B), andI_(C) phase current conductors, respectively, are optionally coupled tothe controller 134. The firmware 136 includes machine instructions fordirecting the controller to carry out operations required for themonitoring device. Memory 138 is used by the controller 134 to storeelectrical parameter data measured by the monitoring device 128.

Instructions from the computer 132 are received by the monitoring device128 via the communications interface 140. Those instructions include,according to an embodiment of the present invention, instructions thatdirect the controller 134 to mark the cycle count, to begin storingelectrical parameter data, or to transmit to the monitoring systemsoftware 132 electrical parameter data stored in the memory 138. Theelectrical parameter data can include any data acquired by monitoringdevices, including any combination of frequency variations, amplitudevariations, and phase variations.

The present invention provides an algorithm that precisely,automatically, and temporally aligns the data from multiple monitoringdevices to the same voltage zero-crossing. Other data alignment aspectsdiscussed below are based on this capability. The data alignment aspectof the present invention is facilitated by functionality in both themonitoring device 128 and the monitoring system software running on thecomputer 132, and the requirements of each will be discussedindividually. Collection and partial analysis of data is performed inthe monitoring device 128.

From the time the monitoring device 128 is energized, a cycle count isperformed of the measured voltage signals. The cycle count issequentially iterated with each positive voltage zero-crossing (or,alternately, with each negative voltage zero-crossing). As themonitoring device 128 measures both the frequency and amplitudevariations of the voltage and current from cycle to cycle, a comparisonis performed to their respective nominal values. The frequency andamplitude variations and associated cycle count are tracked by thedevice firmware 136. The associated monitoring device time at anyspecified cycle count can be stored in the memory 138.

The monitoring system software executed by the computer 132 initiatesalignment of the data associated with multiple monitoring devices bysending a global command to all monitoring devices 128, 130 on the powermonitoring system 120 to mark their cycle count, time and buffer apredetermined amount of cycle-by-cycle data.

This predetermined amount of data is established based on the number ofmonitoring devices in the power monitoring system, the communicationstime delays in the power monitoring system and the magnitude offrequency and amplitude variations. When the buffering is complete, themonitoring devices 128, 130 transmit their buffered data to the computer132.

Once the data is collected by the monitoring devices 128,130, themonitoring system software uploads the buffered data for analysis. Therewill likely be a time offset in each monitoring device's buffered databecause the monitoring devices on the system will likely not beginbuffering the data simultaneously due to communications time delays inthe power monitoring system and internal time delays within themonitoring devices. The buffered data is analyzed by the monitoringsystem software on the computer 132 to locate the highest correlation infrequency between all the monitoring devices 128, 130. Generally, thehighest correlation is located by sliding the buffered frequency data inone monitoring device with respect to another until the frequencyvariations line up with each other as shown in FIG. 4.

The frequency data 360 for the monitoring device 128 is “slid” relativeto the frequency data 362 for the monitoring device 130 until thefrequency data for each device line up. Thus, the zero-crossingassociated with Δt₁ of monitoring device 128 is aligned with thezero-crossing associated with Δt₁ of monitoring device 130, thezero-crossing associated with Δt₂ of monitoring device 128 is alignedwith the zero-crossing associated with Δt₂ of monitoring device 130, andso on. Cross-correlation algorithms for “sliding” two data sets relativeto one another until they are aligned are discussed in further detailbelow in connection with FIGS. 5A and 5B.

Once the buffered data is aligned, the cycle count of the firstmonitoring device 128 is associated with the cycle count of the secondmonitoring device 130 in the software on the computer 132. The on-boardmonitoring device time may optionally also be aligned or associatedrelative to one another. This process is repeated for each monitoringdevice in the power monitoring system 120 until all devices' cyclecounts are associated with each other. During the data alignmentprocess, the monitoring system software on the computer 132 builds amatrix of each device's cycle count and time with respect to each otherand the time on the computer 132.

Although FIG. 2 shows a simplified power monitoring system 120 with justtwo monitoring devices 128, 130, the data alignment embodiments of thepresent invention can be applied to any power monitoring system 120 ofany complexity with multiple hierarchical levels, such as the one-linediagram shown in FIG. 7. For ease of illustration and discussion, onlytwo monitoring devices 128, 130 have been discussed.

Once the data of the two monitoring devices 128, 130 is aligned relativeto one another, there is typically no need to realign the data againunless a monitoring device loses its voltage signal or resets itself. Inthose cases, only the monitoring devices that lose their voltage signalor reset need to be realigned in accordance with the present invention.The data alignment technique of the present invention can be initiatedby an event, such as an undervoltage or overvoltage condition,connecting or disconnecting a load to the power monitoring system, achange in the characteristics of the voltage, current, or a load, amonitoring device reset, or a power loss. The data alignment techniqueof the present invention can also be initiated automatically by themonitoring software or manually by the user.

Turning now to FIG. 5A, a flow chart, which can be implemented as a dataalignment algorithm 180 executed by the computer 132, is shown forcarrying out an embodiment of the present invention. The data alignmentalgorithm 180 begins by sending a message to the monitoring devices(such as monitoring devices 128, 130) to begin buffering data (200)until buffering is complete (202). The computer 132 reads the data fromeach device (204). The data represents, in an embodiment, electricalparameter data such as variations in (fundamental) frequency, variationsin amplitude, and variations in phase. Preferably, the data representsvariations in fundamental frequency. Fundamental frequency is apreferred criterion because it remains unchanged throughout the powermonitoring system, even if transformers are present in the system.Amplitude and phases can shift when transformers are present in thesystem; however, the present invention contemplates using amplitude andphase information as criteria.

The computer 132 selects a reference monitoring device (206) such asmonitoring device 128 and then selects a monitoring device to analyze(208) such as monitoring device 130. Data from the monitoring devices128, 130 is then cross-correlated according to the present invention(210), and each device's cycle count and time relationships are enteredinto a matrix (212). The cross-correlation is carried out by aconventional cross-correlation algorithm, preferably such as the oneprovided below in Equation 1. $\begin{matrix}{{r(d)} = \frac{\sum\limits_{i}\quad\left\lbrack {\left( {{x(i)} - {mx}} \right)*\left( {{y\left( {i - d} \right)} - {my}} \right)} \right\rbrack}{\sqrt{\sum\limits_{i}\quad\left( {{x(i)} - {mx}} \right)^{2}}\sqrt{\sum\limits_{i}\quad\left( {{y\left( {i - d} \right)} - {my}} \right)^{2}}}} & \left( {{Equation}\quad 1} \right)\end{matrix}$

The correlation coefficient is represented by r(d), the delay (offset orshift) being represented by d, where −1<=r(d)<=1 for two series x(i) andy(i) representing the respective data from the monitoring devices 128,130; and mx and my are the means of the corresponding series x(i) andy(i). According to an embodiment, the correlation algorithm is acircular correlation algorithm in which out-of-range indexes are“wrapped” back within range. In another embodiment, the correlationalgorithm is a linear correlation algorithm in which each series isrepeated. In still other embodiments, the correlation algorithm is apattern-matching algorithm or a text-search algorithm.

After cross-correlation, the computer 132 checks whether all monitoringdevices have been analyzed (214), and if so, proceeds to check thewiring of the phase conductors. In many instances, phase conductors maybe misidentified throughout an electrical system by the contractor whoinstalled them. For example, the phase that is identified as “A-phase”at the main switchgear may be identified as “B-phase” at the load. Thisnomenclature misidentification of the phase conductors can result inconfusion, and even pose a safety hazard.

To mitigate this hazard, the computer 132 analyzes the voltage (orcurrent) data by sampling data at the voltage (or current) zero-crossingof a reference channel on each monitoring device (216). The computer 132determines whether the wiring is correct (218) by determining whetherthe values of the sampled data are zero, negative, or positive, and,based on those values, assigning phase notations (such as A, B, or C)for each reference channel. If all monitoring devices are identifiedaccurately, the data values for Phase-A should be approximately zero. Ifthe data values are negative, then the phase in question is the“B-Phase” for an ABC phase rotation. If the data values are positive,then the phase in question is the “C-phase” for an ABC phase rotation.The user is notified (220) whether the wiring is correct. Once theproper phase notation is determined for each monitoring device (222),the computer 132 may then allow the user to correct the misidentifiedphase notation in any or all monitoring devices. The phase diagnosisembodiments according to the present invention are applicable to voltageinputs as well as current inputs.

FIG. 5B illustrates a flow chart for carrying out another embodiment ofthe present invention. As with FIG. 5A, reference will be made to thepower monitoring system 120 shown in FIG. 2 for ease of discussion, butas mentioned before, the data alignment techniques of the presentinvention are applicable to any utility monitoring system.

The computer 132 instructs each monitoring device in the powermonitoring system 120 to store data on a cycle-by-cycle basis (250) fora predetermined number of cycles, preferably between about 1,000 andabout 10,000 cycles. When a sufficient amount of data has been stored bythe monitoring devices, the computer 132 receives the data from themonitoring devices (252) and selects a reference monitoring device(254). Using a convention cross-correlation algorithm such as Equation 1above, the computer 132 calculates a correlation coefficient r(d)between at least a portion of the data (such as about 400 cycles) of thereference monitoring device and the data of a second monitoring device(256). The calculated correlation coefficient is stored, and the data ofthe second monitoring device is shifted relative to the reference deviceby one cycle (258).

As mentioned above, the out-of-range indexes can be wrapped back withinrange according to a circular correlation algorithm or the indexes canbe repeated according to a linear correlation algorithm. A correlationcoefficient is calculated using the shifted data (260) and if no furthershifts are required (262), the data of the second monitoring device isaligned with the data of the reference device at the point at which themaximum correlation coefficient is calculated or at which thecorrelation coefficient exceeds a threshold value, such as 0.5 (264). Itshould be noted that when the correlation coefficient r(d) is close to1.0, the algorithm can exit without conducting any further shifts.

The computer 132 synchronizes the clocks of the second monitoring deviceand the reference device at the point of alignment (266). The computer132 reads the cycle count in each monitoring device and the associatedmonitoring device's on-board clock time. A monitoring device's on-boardclock time and cycle count may drift with respect to each other due tothe limitations of the on-board clock. Once the data is aligned, thecycle count is considered the absolute reference for a monitoringdevice. Due to the clock drift, it may be necessary to re-read the timeassociated with a device's cycle count periodically to reestablish thedevice's time. The software on the computer 132 will then update thematrix containing the monitoring device time information.

Another capability of this feature is to allow all on-board monitoringdevice clocks to be periodically reset to the same value to provide astandard time for the entire power monitoring system. Preferably, thetime within the monitoring system software (running on the computer 132)is set according to some absolute time reference. Once the computer timeis set, the monitoring system software resets the time on all themonitoring devices accordingly. In this embodiment, the data and time ofeach monitoring device and the software would be more accurately alignedwith the absolute time reference.

When there are no further monitoring devices to align (268), theprocedure ends. In an alternate embodiment, all of the monitoringdevice's data is aligned before the clocks are synchronized (266).

Another advantage of the data alignment techniques of the presentinvention is the ability to align data and time on different points ofthe utility grid. If monitoring devices are located on two differentpoints of the same utility grid, it is possible to align the monitoringdevices together. In this embodiment, the monitoring devices at eachgeographic location are first aligned to each other in accordance withthe present invention. The software managing all the systems is thenused as the absolute time reference for all systems, giving them all acommon point of reference.

Referring back to FIG. 1, the integrated monitoring system 100 includesthe hierarchy classification system 106. Having a thorough knowledge ofan electrical power system's layout is essential to understanding andcharacterizing the system. Power meters typically provide only theelectrical system's operating parameters, but do not give information onhow the parameters at different monitoring points on the electricalsystem relate to each other. Having the hierarchy of an electricalsystem puts the operating parameters of multiple monitoring devices intospatial context with each other. This spatial context gives the user amore powerful tool to troubleshoot system problems, improve systemefficiencies, predict failures and degradation, locate the source ofdisturbances, or model system responses.

The hierarchy classification system 106 of the present invention allowsthe monitoring system software to collect data from the monitoringdevice on the utility system 102, and automatically determine thehierarchy of the utility system 102 with little or no user input. Thelevel of detail given by the hierarchy classification system 106directly correlates with the number and extent of monitoring devices inthe utility system 102. As supplemental monitoring devices are added,the auto-learned hierarchical algorithm according to the presentinvention enables them to be automatically incorporated into thedetermined hierarchical structure.

A hierarchy of nodes is based on a relationship that determines that onenode is always greater than another node, when the nodes are related. Ahierarchy's relationship can link or interrelate elements in one ofthree ways: directly, indirectly, or not at all. An illustration of adirect link or interrelationship is shown in FIG. 6 between the Load₂310 and Feeder₂ 306. In contrast, an indirect link exists between Load₂310 and Main₁ 302. Finally, there is effectively no link between theLoad₁ 308 and Load₂ 310 and between Feeder₁ 304 and Feeder₂ 306.

In the case of a power system hierarchy, an objective is to orderelements in the power system so as to represent the true connectionlayout of the power system. Determining the hierarchy of a power systemprovides important information that can be used to solve problems,increase equipment and system performance, improve safety, and savemoney. The level of detail contained in a power system hierarchy willdepend on both the number of elements or nodes that are being monitoredand the node's ability to provide feedback to the auto-leamed hierarchyalgorithm in the monitoring system software running on the computer 132.

Generally, the hierarchy classification system 106 according to thepresent invention utilizes an auto-learned hierarchy algorithm in themonitoring system software that is based on rules and statisticalmethods. Periodically, the monitoring system software polls eachmonitoring device in the utility system 102 to determine certaincharacteristics or parameters of the utility system 102 at that node(represented by monitoring device M). Multiple samples of specifiedparameters are taken from each meter in the system at the same givenpoint in time. Once the parameter data is collected from each node M inthe utility system 102, the auto-learned hierarchy algorithm analyzesthe data and traces the relationships or links among the monitoringdevices with respect to the time the data sample was taken and theassociated value of the data sample. This analysis may be performedperiodically to increase the probability that the hierarchy is accurate,or to ascertain any changes in the hierarchy. Once this iterativeprocess reaches some predetermined level of statistical confidence thatthe determined layout of the utility system 102 is correct, theauto-learned hierarchy algorithm ends. The final layout of the utilitysystem 102 is then presented to the user for concurrence. As eachmonitoring device's data is evaluated over time (the learning period)with respect to all other monitoring devices using the auto-learnedhierarchy algorithm, a basic layout of the hierarchical structure of theutility system 102 is determined based on the monitoring pointsavailable. In this respect, the algorithm according to the presentinvention uses historical trends of the data from each monitoringdevice, and those trends are compared to determine whether anyinterrelationship (link) exists between the monitoring devices. A moredetailed hierarchical structure can be determined with more monitoringpoints available for analysis.

A benefit of the auto-learned hierarchy algorithm of the presentinvention is to provide automatically a basic hierarchical structure ofa utility system being monitored with minimal or no input by the user.The hierarchy can then be used as a tool for evaluation by other systems114. Another benefit is that the present invention improves the accuracyof the time synchronization between the monitoring devices and themonitoring system software.

In an embodiment in which the utility system 102 is a power monitoringsystem, samples of specific electrical parameters (such as power,voltage, current, or the like) are simultaneously taken from eachmonitoring device in the power monitoring system. This parameter data isstored and analyzed with respect to the time the sample is taken, theassociated value of the data point, and the monitoring device providingthe data.

Data taken from each monitoring device in the power monitoring system iscompared with each other to determine whether any correlation existsbetween the monitoring devices. The data is analyzed for statisticaltrends and correlations as well as similarities and differences over apredetermined period of time in accordance with the present invention.

According to an embodiment, one or more rules or assumptions are used todetermine the hierarchical order of the power system. Certainassumptions may have to be made about the utility system in order toauto-learn the utility system's hierarchy. The assumptions are based onOhm's Law, conservation of energy, and working experience with typicalpower distribution and power monitoring systems.

General rules that may be made by the auto-learned hierarchy algorithmin connection with power systems and power monitoring systems include:

1. The power system being analyzed is in a single 320 (FIG. 7) ormultiple radial feed configuration 330 (FIG. 8).

2. The meter measuring the highest energy usage is assumed to be at thetop of the hierarchical structure (e.g., Main 322 shown in FIG. 7).

3. The rate of sampling data by the meters is at least greater than theshortest duty cycle of any load.

4. Energy is consumed (not generated) on the power system during theparameter data collection process.

5. The error due to the offset of time in all meters on the powermonitoring system is minimal where data is pushed from the monitoringdevice to the monitoring system software running on the computer 132.

The following additional parameters may be present for the auto-learnedhierarchy algorithm:

1. Data is not collected for hierarchical purposes from two monitoringdevices installed at the same point of a power system.

2. Meters with no load are ignored or only use voltage information todetermine their position in the hierarchy.

3. Multiple mains (Main1, Main2, Main3, etc.) may exist in the powersystem.

4. Data is provided to the monitoring system software by each monitoringdevice in the system.

5. Loads that start or stop affect the load profiles for anycorresponding upstream metered data with a direct or indirect link tothat load.

6. Voltage characteristics (fundamental, harmonic, symmetricalcomponents) are relatively consistent for all monitoring devices on thesame bus.

7. Transformer losses on the electrical system are minimal with respectto the loads downstream from the transformer.

8. General correlation (over time) of loads between monitoring devicesindicates either a direct or indirect link.

9. Multiple unmetered loads at a point in the power system areaggregated into a single unknown load.

Any of the foregoing assumptions and parameters can be combined for aradial-fed electrical power system. For example, in a specificembodiment, the following rule-based assumptions and parameters can beutilized:

1. Voltages and currents are higher the further upstream (closer to thetop of the hierarchy) a monitoring device is.

2. Harmonic values are generally lower the further upstream a monitoringdevice is.

3. Transformers can vary the voltages and currents.

4. Total power flow is higher upstream than downstream.

5. The power system is a radial-fed system.

6. Two monitoring devices will not be installed at the same point.

7. Monitoring devices with the same voltage distortion are adjacentlyconnected.

8. The total load measured at a specific hierarchical level is equal(excluding losses) to the sum of all measured and unmeasured loadsdirectly linked to that hierarchical level.

Monitoring devices are considered to be on the same hierarchical levelif they are all directly linked to the same reference device. Forexample, referring to FIG. 7, a simplified one-line diagram of a utilitymonitoring system 320 is shown having five distinct levels representedby 323 a,b,c,d,e. In the specific case of a power monitoring system,each level represents a feeder to which multiple monitoring devices canbe directly linked. All monitoring devices directly linked to a feederare considered to be on the same feeder level. Thus, the main 322 isdirectly linked to the feeder 323 a, and thus exists on its own level inthe hierarchy. Feeder 323 b directly links to three monitoring devices,and therefore comprises another distinct level. Feeder 323 c comprisesanother level distinct from feeders 323 a and 323 b because themonitoring devices directly linked to feeder 323 c are not directlylinked to feeders 323 a or 323 b. In the case of a water, air, gas, andsteam systems, each level may be represented by a header instead of afeeder.

A specific aspect of the auto-learned hierarchy algorithm 400 inaccordance with an embodiment of the present invention is flow-chartedin FIGS. 9-11A. The algorithm 400 first checks whether there is morethan one monitoring device in the system (402), and if not, thealgorithm ends. If more than one monitoring device is present,electrical data is taken from each monitoring device (M₁, M₂, . . . ,M_(k)) and compiled into a Data Table (404). The Data Table tabulatesthe raw data (such as power, voltage magnitude, voltage distortion,current magnitude, current distortion, or symmetrical component data)taken at regular intervals (T₁, T₂, . . . , T_(n)) over a given timeperiod. The time period between samples depends on the shortest dutycycle of any load in the power monitoring system. The maximum timeperiod (T_(n)) is determined based on the level of variation of eachmonitoring device's load in the power monitoring system. The monitoringdevice with the maximum power in the Data Table is assumed to be a Main(i.e., highest level in the electrical hierarchy) (408). However, thepresent invention also contemplates multiple hierarchies (i.e., multipleMains). An example of the Data Table is shown in Table 1 below. TABLE 1Data Table Example Time Meter 1 Meter 2 Meter 3 Meter 4 . . . Meter k T₁D₁₁ D₂₁ D₃₁ D₄₁ . . . D_(k1) T₂ D₁₂ D₂₂ D₃₂ D₄₂ . . . D_(k2) T₃ D₁₃ D₂₃D₃₃ D₄₃ . . . D_(k3) T₄ D₁₄ D₂₄ D₃₄ D₄₄ . . . D_(k4) . . . . . . . . . .. . . . . . . . . . . T_(n) D_(1n) D_(2n) D_(3n) D_(4n) . . . D_(kn)

Once the data for the Data Table is accumulated, a Check Matrix isdeveloped. The Check Matrix is a matrix of logical connections based onthe Data Table. A zero (0) indicates that no direct link exists betweenany two monitoring devices, and a one (1) indicates that there is apossible relationship between two monitoring devices. An exemplary CheckMatrix is illustrated in Table 2 below. In Table 2, it is assumed thatno link exists between Meter 1 and Meter 2. This is because the powermeasured by Meter 1 exceeds Meter 2 in one entry of the Data Table andthe power measured by Meter 2 exceeds Meter 1 in another entry of theData Table. Meter 1 always correlates with itself so an NA is placed inthat cell of the Check Matrix. Only half of the Check Matrix is requireddue to the redundancy of information. TABLE 2 Check Matrix Example Meter1 Meter 2 Meter 3 Meter 4 . . . Meter k Meter 1 NA 0 1 1 . . . 0 Meter 20 NA 1 0 . . . 1 Meter 3 1 1 NA 0 . . . 1 Meter 4 1 0 0 NA . . . 0 . . .. . . . . . . . . . . . . . . . . . . . . . . . Meter k 0 1 0 . . . NA

Once the Check Matrix is determined, the data from each monitoringdevice in the Data Table is used to develop a Correlation CoefficientMatrix (CCM) shown in Table 3 below. In the CCM, a statisticalevaluation is carried out to determine the linear relationship of eachmonitoring device in the electrical system with respect to the othermonitoring devices in the matrix. The correlation coefficient betweenany two monitoring devices is determined and placed in the appropriatecell in the CCM. In the exemplary Table 3 below, C₁₂ is the correlationcoefficient of Meter 1 with respect to Meter 2. The higher thecorrelation coefficient value is, the higher the probability that thesetwo monitoring devices are either directly or indirectly linked.Conversely, the lower this number is, the lower the probability thatthese two monitoring devices are directly or indirectly linked. Equation2 below is used to determine the correlation coefficient between any twogiven monitoring devices: $\begin{matrix}{\rho_{x,y} = \frac{{Cov}\left( {x,y} \right)}{\sigma_{x}\sigma_{y}}} & \left( {{Equation}\quad 2} \right)\end{matrix}$where: ρ_(x,y) is the correlation coefficient and lies in the range of−1≦ρ_(x,y)≦1; Cov(x,y) is the covariance of x and y; and σ_(x) and σ_(y)are the standard deviations of x and y, respectively. $\begin{matrix}{{{Cov}\left( {x,y} \right)} = {\frac{1}{n}{\sum\limits_{j = 1}^{n}\quad{\left( {x_{j} - \mu_{y}} \right)\left( {y_{j} - \mu_{y}} \right)}}}} & \left( {{Equation}\quad 3} \right)\end{matrix}$where: n is the number of data elements in x and y, and μ_(x) and μ_(y)are the mean values of x and y respectively.

The diagonal cells of the Correlation Matrix are all always 1 becauseeach meter has 100% correlation with itself. Again, only half of theCorrelation Matrix is required due to the redundancy of data (e.g.,C₁₂=C₂₁). TABLE 3 Correlation Coefficient Matrix (CCM) Example Meter 1Meter 2 Meter 3 Meter 4 . . . Meter k Meter 1 1 C₁₂ C₁₃ C₁₄ . . . C_(1k)Meter 2 C₂₁ 1 C₂₃ C₂₄ . . . C_(2k) Meter 3 C₃₁ C₃₂ 1 C₃₄ . . . C_(3k)Meter 4 C₄₁ C₄₂ C₄₃ 1 . . . C_(4k) : : : : : 1 : Meter k C_(k1) C_(k2)C_(k3) C_(k4) . . . 1

Returning to FIG. 9, a list of meters is developed for each level of thehierarchy under consideration. The top-most level is assumed to be themeter with the largest power reading, which is assumed to be a main.Once that meter is found in the Data Table (408), the algorithm 400places the main in a feeder level list of the hierarchy and clears thelist of monitoring devices on the current feeder level in the hierarchy(410). In subsequent iterations through the MAIN LOOP, the algorithm 400places the reference meter in the previous feeder level list of thehierarchy. It should be understood that on the first iteration, there isno previous level list. The algorithm 400 clears a Correlation ReferenceArray (CRA) (412), and designates the main as the reference monitoringdevice (414). An exemplary CRA is shown in Table 4, below, for niterations for a given feeder level. C₅₁ corresponds to the correlationcoefficient between meter 5 (the reference meter) and meter 1, C₅₂corresponds to the correlation coefficient between meter 5 and meter 2,and so forth. Initially, the CRA is cleared for each feeder level, andthe algorithm 400 develops a new CRA for each feeder level by populatingeach iteration column with correlation coefficients for all meters onthe current feeder level. A specific example is explained in connectionwith Table 5 below.

The Correlation Coefficient Matrix (CCM) is calculated based on thepower data (416). In the first iteration, the only known element in thehierarchy is the main, and the hierarchy is auto-learned from thetop-most feeder level down, in accordance with some or all of theassumptions or parameters listed above. TABLE 4 Correlation ReferenceArray (CRA) Example Iteration Iteration 1 Iteration 2 Iteration 3Iteration 4 Iteration 5 . . . n C₅₁ C₅₁ C₅₁ C₅₁ C₅₁ . . . C₅₁ C₅₂ C₅₂C₅₂ C₅₂ C₅₂ . . . C₅₂ C₅₃ C₅₃ C₅₃ C₅₃ C₅₃ . . . C₅₃ C₅₄ C₅₄ C₅₄ C₅₄ C₅₄. . . C₅₄ . . . . . . . . . . . . . . . . . . . . . C_(5m) C_(5m) C_(5m)C_(5m) C_(5m) . . . C_(5m)

Continuing with FIG. 10, the algorithm 400 zeros the correlationcoefficients in the CCM for meters that have zeros in the Check Matrixand meters that have already been found to be connected (418). Thecolumn for the reference monitoring device is copied from the CCM to theCRA (420). A specific example will be explained next in connection withTable 5 below. Assume that meter 5 in the CCM is designated as thereference meter (414). The algorithm 400 calculates the CCM based on theData Table (416) and zeroes the correlation coefficient(s) in the CCMfor meters that have zero in the Check Matrix and meters that have beenfound to be connected (418). The column in the CCM corresponding tometer 5 is copied into the column Iteration 1 of the CRA. Referring toTable 5, meter 11 has the highest correlation with meter 5 of 0.649, andmeter 11 is marked as connected with meter 5 for the current feederlevel.

In Iteration 2, meter 11's power is subtracted from meter 5's power inthe data table, and the meter 5-11 correlation coefficient drops to−0.048 in Iteration 2, which provides a high degree of confidence thatmeter 11 is interrelated with meter 5. Also noteworthy is that somemeter's correlation coefficients trend higher as the iterationsprogress. For example, the correlation coefficients for meter 18relative to meter 5 gradually increase from 0.296 in Iteration 1 to0.417 in Iteration 2 to 0.436 in Iteration 3 to 0.525 in Iteration 4 andfinally to 0.671 in Iteration 5, which is the highest correlationcoefficient among all the meters (meter 5 correlated with itself isalways 1.0, so its correlation coefficient is ignored). This increasingtrend also provides a high degree of confidence that meter 18 is alsodirectly linked with meter 5, and this link is finally confirmed inIteration 5. The same increasing trends can be observed for meters 12and 15, for example. In Iteration 7, none of the correlationcoefficients exceed a threshold, and the algorithm 400 proceeds toanalyze the next feeder level. By Iteration 7, the algorithm 400 hasdetermined that meters 11, 12, 14, 15, 18, and 20 are directly linkedwith meter 5. TABLE 5 CRA Example With Exemplary CorrelationCoefficients Iteration 1 Iteration 2 Iteration 3 Iteration 4 Iteration 5Iteration 6 Iteration 7 5-1 0.020 −0.029 0.010 0.016 −0.037 −0.004 0.0075-2 0.043 −0.020 −0.037 −0.009 −0.095 −0.091 −0.099 5-3 0.067 0.0790.017 0.024 −0.052 −0.046 −0.009 5-4 0.018 −0.024 −0.038 −0.018 0.0370.015 0.037 5-5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 5-6 0.0580.022 −0.016 −0.015 −0.035 −0.010 0.029 5-7 −0.042 −0.005 0.001 0.0540.033 0.026 0.031 5-8 −0.034 −0.016 −0.057 −0.058 0.005 −0.034 −0.0495-9 0.418 0.386 0.308 0.292 0.189 0.099 0.136 5-10 0.022 0.077 0.0160.014 −0.016 −0.018 0.022 5-11 0.649 −0.048 −0.090 −0.095 −0.076 −0.077−0.014 5-12 0.344 0.506 0.628 0.725 0.047 −0.007 0.016 5-13 −0.038−0.036 0.038 0.017 −0.046 −0.023 −0.010 5-14 0.483 0.591 0.072 0.0440.066 −0.006 0.004 5-15 0.043 0.161 0.210 0.263 0.417 0.587 0.031 5-160.024 0.045 0.055 0.044 −0.017 −0.010 0.022 5-17 −0.057 −0.063 −0.101−0.090 −0.061 −0.048 −0.049 5-18 0.296 0.417 0.436 0.525 0.671 0.1130.165 5-19 −0.046 −0.053 −0.057 −0.047 −0.046 −0.050 −0.034 5-20 0.3980.549 0.633 0.128 0.069 0.054 0.061 5-21 −0.060 −0.017 0.028 0.080−0.013 0.010 0.005

Still referring to FIG. 10, the algorithm 400 finds the monitoringdevice (feeder) in the CRA that has the highest correlation with thereference monitoring device (422). If the correlation does not exceed athreshold (0.5 in a preferred embodiment), the algorithm 400 continuesto FIG. 11A (OP3), such as in the case of Iteration 7 in Table 5 shownabove.

Otherwise, the algorithm 400 determines whether the current iteration isthe first iteration for the reference monitoring device (426), and ifnot, determines whether the feeder correlation is trending higher (428).If the feeder correlation is not trending higher, the algorithm 400continues to FIG. 11A (OP3). A higher trend is an indication that themonitoring device is likely on the current level of the hierarchy underconsideration.

If the current iteration is the first iteration for the referencemonitoring device, the feeder is added to the list of monitoring deviceson the current level of the hierarchy (430), and the algorithm 400continues to FIG. 11A (OP2). The reference monitoring device and thefeeder are designated as directly linked (or interrelated) in aconnection table (446), and the power associated with the feeder issubtracted from the reference monitoring device in the data table (448).The connection table maintains a list of devices and theirinterrelationships (for example, whether they are directly linked). Bysubtracting the power of the feeder associated with the highestcorrelation coefficient relative to the reference monitoring device,other feeders (monitoring devices) connected to the reference monitoringdevice will see their correlation coefficients increase. The algorithm400 returns to the FEEDER LOOP of FIG. 9, and the next iterationcontinues with the remaining monitoring devices.

Turning now to the OP3 function, the algorithm 400 determines whetherall monitoring devices on the previous level have been analyzed (432),and if not, the next monitoring device (feeder) is obtained on theprevious level, and the algorithm 400 returns to the FEEDER LOOP of FIG.9. If all monitoring devices on the previous level have been analyzed,the algorithm 400 checks whether a connection has been found for allmonitoring devices in the hierarchy (434). If so, the algorithm 400exits. If not, the algorithm 400 checks whether the highest correlationcoefficient in the CCM exceeds a threshold (436). If not, the algorithm400 exits. If so, the algorithm 400 determines whether any moremonitoring devices are found for the current level (438). If not, thealgorithm 400 returns to the MAIN LOOP in FIG. 9. If so, the algorithmmoves the monitoring devices on the current level to the previous level(440) and clears the CRA (442). The algorithm returns to the FEEDER LOOPof FIG. 9 to determine the relationships among the remaining monitoringdevices on the current level.

An auto-learned hierarchy algorithm 500 according to another embodimentof the present invention is illustrated in FIG. 11B. The algorithm 500starts by receiving from each monitoring device a criterion associatedwith each monitoring device (502). The criterion can be an electricalparameter, such as power, voltage, current, current distortion, voltagedistortion, or energy, or a parameter associated with any WAGES utility,such as volume (BTU, MBTU, gallons, cubic feet) per unit time. Themonitoring devices can be power monitoring devices. For example, whenthe criterion is a voltage distortion, monitoring devices on the samelevel of the hierarchy will have roughly the same voltage distortion.Additionally or alternatively, the algorithm can use the harmonicdistortion values to verify the hierarchy determined by the correlationsbased on power criteria. Harmonic distortion can also be used by thealgorithm to better predict unknown candidates with greater accuracy.For example, a monitoring device may be marginally correlated with areference device such that the algorithm cannot determine whether adirect link exists or not. Harmonic distortion can rule in or rule out apotential interrelationship depending upon the harmonic distortionvalues of the neighboring devices on the same level as the monitoringdevice in question. For example, a different harmonic distortionreturned for the monitoring device in question could rule it out asbeing directly linked with a device on the previous level.

The algorithm 500 calculates a correlation coefficient between areference monitoring device and every other monitoring device to beinterrelated in the hierarchy (504). The algorithm 500 determines thehighest correlation coefficient (506) and interrelates the monitoringdevice associated with the highest correlation coefficient and thereference monitoring device (508). The algorithm 500 checks whether moremonitoring devices are to be interrelated (510), and if not, thealgorithm 500 ends. If so, the algorithm 500 checks whether to use thesame reference monitoring device (512), and if so, recalculates thecorrelation coefficients (504). Otherwise, the algorithm 500 selects anew reference monitoring device (514), and recalculates the correlationcoefficients (504).

An auto-learned hierarchy algorithm 550 according to still anotherembodiment of the present invention is illustrated in FIG. 11C. Thealgorithm 550 starts by receiving electrical parameter data from eachmonitoring device at periodic time intervals (552). The algorithm 550arranges the electrical parameter data into a data table that tabulatesthe parameter data at each time interval (554). A correlation matrix isformed that includes correlation coefficients between combination pairsof monitoring devices (556). The algorithm 550 identifies aninterrelationship between a combination pair (558) and removes from thedata table the power associated with the monitoring device for which aninterrelationship was identified (560). If no more interrelationshipsare to be identified (562), the algorithm 550 ends. Otherwise, itrecalculates correlation coefficients among the remaining combinationpairs (564) and identifies another interrelationship between theremaining combination pairs (558). This process is repeated until allinterrelationships among the monitoring devices have been identified.

The auto-learned hierarchy algorithm according to the variousembodiments of the present invention is operable in both radial-fed andmultiple radial-fed systems. In multiple radial-fed systems, thealgorithm first determines the main meter having the highest power, thendetermines the hierarchy for that system first before proceeding to thenext system(s) having lower power ratings.

The auto-learned hierarchy algorithm has been discussed in variousembodiments in which the hierarchy is developed from the top-most leveltowards the bottom-most level. In an alternate embodiment, anauto-learned hierarchy algorithm develops a hierarchy from thebottom-most level based on events local to each level. For example,monitoring devices proximate to an event will ‘see’ an event, such as aload turning on or off, before monitoring devices remote from the eventwill see it. The algorithm recognizes interrelationships amongmonitoring devices based on the occurrences of events and the timestampsassociated with each monitoring device as to when it became aware of anevent. By mapping out a chronology of when each monitoring device in thesystem perceives an event, conclusions can be automatically drawn basedupon the time order in which monitoring device perceived that event asto which meters are interrelated (directly linked).

Referring back to FIG. 1, the automated data integrated monitoringsystem 100 produces contextual data 108 from the data alignment system104 and the hierarchy classification system 106. The contextual data 108contains the data from each monitoring device in context with everyother monitoring device and is thus more valuable to the user.Contextual analysis of the measured data can be performed, whichinvolves an assessment of the data such that specific externalparameters from each monitoring device are aligned or are made known.The primary external parameters of concern include:

The temporal position of each monitoring device's data in the utilitysystem 102 relative to every other monitoring device's data in theutility system 102; and

The spatial position of each monitoring device M in the utility system102 with respect to every other monitoring device M in the utilitysystem 102.

Evaluating all the monitoring data accumulated from the utility system102 in context will provide a degree of knowledge about the utilitysystem 102 that heretofore was unavailable. Because the information fromthe entire system (software and monitoring devices) is integratedtogether through a uniform context, this approach to monitoring autility system is referred to as Integrated Monitoring (IM).

A useful analogy of the IM approach according to the present inventionis the central nervous system of the human body. The brain (software)knows what is going on with the entire body (the monitoring devices)relative to time and position. If a toe is stubbed, the brain sends asignal for the body to react in some manner. Similarly if an electricalevent occurs, the IM algorithms executed by the monitoring systemsoftware provides useful information to the user on the symptomsthroughout the monitored system, potential sources of the problem, andpossible solutions or recommendations.

The present invention involves integrating data based on analysis of thedata from each monitoring point using special algorithms (for example, adata alignment algorithm and an auto-learned hierarchy algorithm) in themonitoring system software. In the data alignment system 104, subtle butmeasurable changes in the data's frequency and amplitude are analyzedfrom all data sources. These changes are used to establish both thecommon point of data alignment for all data sources and a data source'sposition in the electrical system with respect to other data sources.Because the process of integrating the system data is performedautomatically on algorithms in the monitoring system software, much ofthe effort and expense required by the user is eliminated. Morearbitrary and substantial variations of the parameters being analyzedoffers quicker integration of the system data.

There are several benefits associated with IM that are beyond what ispresently available including:

The automated IM approach greatly reduces the existing requirements forthe user to manually provide detailed information about the power systemlayout in order to put the system data into context. The IM algorithmsanalyze data from each monitoring point in the electrical system toautomatically determine the system layout with little or no userinvolvement, saving the user time and resources.

The automated IM approach eliminates the need for special hardware,additional data lines, and, in some cases, monitor accessories. The IMalgorithms analyze data from each monitoring point in the electricalsystem to automatically determine the temporal alignment of the systemdata, saving the user equipment and labor costs.

The automated IM approach allows an easier configuration of monitoringhardware and software. This is because the IM algorithms automaticallyput the monitoring information into context throughout the system. Oncethe monitoring devices are in context, additional decisions regardinghardware and software configuration can automatically be made by the IMalgorithms. One example would be setting a monitoring device'sunder-voltage threshold depending on the monitoring device's locationwithin the electrical system. Again, the automated IM approach saves theuser time and resources.

An automated IM algorithm 600 according to an embodiment of the presentinvention is illustrated in FIG. 12. The algorithm 600 starts by sendinga command to the monitoring devices to collect frequency data (602).Data from the monitoring devices is uploaded to the host computer (604)and the data from all the monitoring devices is aligned (606) inaccordance with the present invention. When all the data is aligned, thealgorithm 600 determines whether the power system layout is complete(610). If so, the algorithm 600 ends, and the contextual data can beused in further software applications.

If the power system layout is not complete, the algorithm 600 sends acommand to the monitoring devices to collect power data (612). The hostcomputer running the algorithm 600 uploads the power data frommonitoring devices (614) and determines the power system layout (616) inaccordance with the present invention. This procedure is repeated untilthe power system layout is complete (618) at which point the algorithmends.

While particular embodiments and applications of the present inventionhave been illustrated and described, it is to be understood that theinvention is not limited to the precise construction and compositionsdisclosed herein and that various modifications, changes, and variationscan be apparent from the foregoing descriptions without departing fromthe spirit and scope of the invention as defined in the appended claims.

1. A method of aligning data measured by monitoring devices coupled to apower monitoring system, comprising: receiving reference signal datafrom a reference monitoring device, said reference signal datarepresenting at least frequency variations measured by said referencemonitoring device for a predetermined number of cycles; receiving secondsignal data from at least a second monitoring device, said second signaldata representing at least frequency variations measured by said secondmonitoring device for a predetermined number of cycles; andautomatically aligning said reference signal data with said secondsignal data.
 2. The method of claim 1, wherein said automaticallyaligning includes: computing a correlation coefficient produced by across-correlation algorithm using at least part of said reference signaldata and at least part of said second signal data; determining whether amaximum correlation coefficient is produced by shifting said secondsignal data relative to said reference signal data and computing acorrelation coefficient produced by said cross-correlation algorithmusing said shifted second signal data and said reference signal data;and repeating said determining until a maximum correlation coefficientis produced by said cross-correlation algorithm.
 3. The method of claim2, wherein said cross-correlation algorithm is a circularcross-correlation algorithm, a linear cross-correlation algorithm, or apattern-matching algorithm.
 4. The method of claim 1, further comprisingcommunicating an instruction to said reference monitoring device tobuffer said reference signal data for said predetermined number ofcycles.
 5. The method of claim 1, further comprising: providingreference time data; receiving first time data from said referencemonitoring device; and responsive to said automatically aligning,synchronizing said first time data with said reference time data.
 6. Themethod of claim 1, further comprising: receiving first time data fromsaid reference monitoring device; receiving second time data from saidsecond monitoring device; responsive to said automatically aligning,synchronizing said first time data with said second time data.
 7. Themethod of claim 1, further comprising: responsive to said automaticallyaligning, sampling data at the zero-crossing of a reference channelassociated with said reference monitoring device; determining whetherthe values of said sampled data are zero, negative, or positive;assigning phase notations based on said determining; and displayinginformation representing said phase notations to the user.
 8. The methodof claim 7, further comprising alerting said user when at least one ofsaid phase notations is misidentified on a phase conductor.
 9. Themethod of claim 1, wherein said reference monitoring device and saidsecond monitoring device are meters.
 10. The method of claim 1, whereinsaid frequency variations represented by said first signal data arevariations in fundamental frequency or variations in harmonic frequency,wherein said variations are associated with a voltage or a current. 11.The method of claim 1, wherein said first signal data represents atleast amplitude variations and said second signal data represents atleast amplitude variations.
 12. A computer readable medium encoded withinstructions for directing a controller to perform the method ofclaim
 1. 13. A method of aligning data in a power monitoring system,comprising: receiving from a first of at least two monitoring devicesfirst signal data corresponding to signal data stored by said firstmonitoring device, said first signal data representing frequency oramplitude variations; receiving from a second of said at least twomonitoring devices second signal data corresponding to signal datastored by said second monitoring device, said second signal datarepresenting frequency or amplitude variations; and aligning said firstsignal data with said second signal data by shifting in increments saidsecond signal data relative to said first signal data until a maximumcross-correlation coefficient is computed by a cross-correlationfunction that calculates a cross-correlation coefficient at each of saidincrements.
 14. The method of claim 13, further comprising communicatingan instruction to said at least two monitoring devices of said powermonitoring system to store said first signal data and said second signaldata, respectively, for a predetermined period of time;
 15. The methodclaim 13, further comprising communicating an instruction to said atleast two monitoring devices to mark their respective cycle counts. 16.The method of claim 13, wherein said first signal data and said secondsignal data further represent phase variations.
 17. The method of claim13, wherein said aligning is carried out by a controller communicativelycoupled to said at least two monitoring devices.
 18. The method of claim13, wherein said at least two monitoring devices are power meters. 19.The method of claim 13, further comprising: responsive to said aligning,synchronizing a first clock in said first monitoring device with asecond clock in said second monitoring device such that at the point ofalignment said first clock and said second clock are temporally aligned.20. The method of claim 13, further comprising providing a referenceclock; and responsive to said aligning, resetting a clock in said firstmonitoring device or said second monitoring device to said referenceclock.
 21. The method of claim 20, wherein said reference clock isprovided via the Internet or a GPS receiver.
 22. The method of claim 13,wherein said aligning is carried out responsive to is an event detectedby said power monitoring system.
 23. The method of claim 22, whereinsaid event is at least one event selected from the group consisting of achange in the characteristics of the voltage or current, a change in thecharacteristics of the load, a reset of a monitoring device, and a powerloss.
 24. A power monitoring system for aligning data, comprising: asystem controller; a first monitoring device having a communicationsinterface coupled to said system controller, a memory, and a controller;and a second monitoring device having a communications interface coupledto said system controller, a memory, and a controller, wherein saidsystem controller is programmed to communicate an instruction to saidfirst monitoring device and said second monitoring device via theirrespective communications interfaces to store in their respectivememories data representing one or both of frequency variations andamplitude variations on a cycle-by-cycle basis for a predeterminednumber of cycles, receive from said first monitoring device first datacorresponding to said data stored by said first monitoring device in itsmemory, receive from said second monitoring device second datacorresponding to said data stored by said second monitoring device inits memory, and align said first data with said second data by shiftingin cycle increments said second data relative to said first data until amaximum cross-correlation coefficient is computed by a cross-correlationfunction that computes a cross-correlation coefficient at each of saidcycle increments, and wherein said controller of said first monitoringdevice is programmed to receive said instruction via said communicationinterface of said first monitoring device, store said first data in saidmemory of said first monitoring device for said predetermined number ofcycles, communicate said first data to said system controller via saidcommunication interface.
 25. The method of claim 24, wherein said firstmonitoring and second monitoring devices are meters.